forked from phoenix/litellm-mirror
feat add support for alist_batches
This commit is contained in:
Ishaan Jaff
parent
36dca6bcce
commit
43a06f408c
2 changed files with 177 additions and 30 deletions
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@ -20,10 +20,8 @@ import httpx
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import litellm
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from litellm import client
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from litellm.utils import supports_httpx_timeout
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from ..llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
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from ..types.llms.openai import (
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from litellm.llms.openai import OpenAIBatchesAPI, OpenAIFilesAPI
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from litellm.types.llms.openai import (
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Batch,
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CancelBatchRequest,
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CreateBatchRequest,
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@ -34,7 +32,8 @@ from ..types.llms.openai import (
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HttpxBinaryResponseContent,
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RetrieveBatchRequest,
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)
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from ..types.router import *
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from litellm.types.router import GenericLiteLLMParams
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from litellm.utils import supports_httpx_timeout
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####### ENVIRONMENT VARIABLES ###################
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openai_batches_instance = OpenAIBatchesAPI()
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@ -314,17 +313,139 @@ def retrieve_batch(
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raise e
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def cancel_batch():
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pass
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async def alist_batches(
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after: Optional[str] = None,
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limit: Optional[int] = None,
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custom_llm_provider: Literal["openai"] = "openai",
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metadata: Optional[Dict[str, str]] = None,
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extra_headers: Optional[Dict[str, str]] = None,
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extra_body: Optional[Dict[str, str]] = None,
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**kwargs,
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) -> Batch:
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"""
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Async: List your organization's batches.
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"""
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try:
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loop = asyncio.get_event_loop()
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kwargs["alist_batches"] = True
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# Use a partial function to pass your keyword arguments
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func = partial(
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list_batches,
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after,
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limit,
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custom_llm_provider,
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extra_headers,
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extra_body,
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**kwargs,
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)
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# Add the context to the function
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ctx = contextvars.copy_context()
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func_with_context = partial(ctx.run, func)
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init_response = await loop.run_in_executor(None, func_with_context)
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if asyncio.iscoroutine(init_response):
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response = await init_response
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else:
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response = init_response # type: ignore
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return response
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except Exception as e:
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raise e
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def list_batch():
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pass
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def list_batches(
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after: Optional[str] = None,
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limit: Optional[int] = None,
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custom_llm_provider: Literal["openai"] = "openai",
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extra_headers: Optional[Dict[str, str]] = None,
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extra_body: Optional[Dict[str, str]] = None,
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**kwargs,
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):
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"""
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Lists batches
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List your organization's batches.
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"""
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try:
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optional_params = GenericLiteLLMParams(**kwargs)
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if custom_llm_provider == "openai":
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# for deepinfra/perplexity/anyscale/groq we check in get_llm_provider and pass in the api base from there
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api_base = (
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optional_params.api_base
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or litellm.api_base
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or os.getenv("OPENAI_API_BASE")
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or "https://api.openai.com/v1"
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)
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organization = (
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optional_params.organization
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or litellm.organization
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or os.getenv("OPENAI_ORGANIZATION", None)
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or None # default - https://github.com/openai/openai-python/blob/284c1799070c723c6a553337134148a7ab088dd8/openai/util.py#L105
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)
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# set API KEY
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api_key = (
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optional_params.api_key
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or litellm.api_key # for deepinfra/perplexity/anyscale we check in get_llm_provider and pass in the api key from there
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or litellm.openai_key
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or os.getenv("OPENAI_API_KEY")
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)
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### TIMEOUT LOGIC ###
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timeout = (
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optional_params.timeout or kwargs.get("request_timeout", 600) or 600
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)
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# set timeout for 10 minutes by default
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async def acancel_batch():
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if (
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timeout is not None
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and isinstance(timeout, httpx.Timeout)
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and supports_httpx_timeout(custom_llm_provider) == False
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):
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read_timeout = timeout.read or 600
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timeout = read_timeout # default 10 min timeout
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elif timeout is not None and not isinstance(timeout, httpx.Timeout):
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timeout = float(timeout) # type: ignore
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elif timeout is None:
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timeout = 600.0
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_is_async = kwargs.pop("alist_batches", False) is True
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response = openai_batches_instance.list_batches(
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_is_async=_is_async,
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after=after,
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limit=limit,
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api_base=api_base,
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api_key=api_key,
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organization=organization,
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timeout=timeout,
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max_retries=optional_params.max_retries,
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)
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else:
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raise litellm.exceptions.BadRequestError(
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message="LiteLLM doesn't support {} for 'create_batch'. Only 'openai' is supported.".format(
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custom_llm_provider
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),
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model="n/a",
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llm_provider=custom_llm_provider,
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response=httpx.Response(
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status_code=400,
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content="Unsupported provider",
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request=httpx.Request(method="create_thread", url="https://github.com/BerriAI/litellm"), # type: ignore
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),
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)
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return response
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except Exception as e:
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raise e
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pass
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async def alist_batch():
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pass
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def cancel_batch():
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pass
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async def acancel_batch():
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pass
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@ -2602,26 +2602,52 @@ class OpenAIBatchesAPI(BaseLLM):
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response = openai_client.batches.cancel(**cancel_batch_data)
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return response
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# def list_batch(
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# self,
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# list_batch_data: ListBatchRequest,
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# api_key: Optional[str],
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# api_base: Optional[str],
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# timeout: Union[float, httpx.Timeout],
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# max_retries: Optional[int],
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# organization: Optional[str],
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# client: Optional[OpenAI] = None,
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# ):
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# openai_client: OpenAI = self.get_openai_client(
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# api_key=api_key,
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# api_base=api_base,
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# timeout=timeout,
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# max_retries=max_retries,
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# organization=organization,
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# client=client,
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# )
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# response = openai_client.batches.list(**list_batch_data)
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# return response
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async def alist_batches(
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self,
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openai_client: AsyncOpenAI,
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after: Optional[str] = None,
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limit: Optional[int] = None,
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):
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verbose_logger.debug("listing batches, after= %s, limit= %s", after, limit)
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response = await openai_client.batches.list(after=after, limit=limit)
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return response
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def list_batches(
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self,
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_is_async: bool,
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api_key: Optional[str],
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api_base: Optional[str],
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timeout: Union[float, httpx.Timeout],
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max_retries: Optional[int],
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organization: Optional[str],
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after: Optional[str] = None,
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limit: Optional[int] = None,
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client: Optional[OpenAI] = None,
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):
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openai_client: Optional[Union[OpenAI, AsyncOpenAI]] = self.get_openai_client(
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api_key=api_key,
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api_base=api_base,
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timeout=timeout,
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max_retries=max_retries,
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organization=organization,
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client=client,
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_is_async=_is_async,
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)
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if openai_client is None:
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raise ValueError(
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"OpenAI client is not initialized. Make sure api_key is passed or OPENAI_API_KEY is set in the environment."
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)
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if _is_async is True:
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if not isinstance(openai_client, AsyncOpenAI):
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raise ValueError(
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"OpenAI client is not an instance of AsyncOpenAI. Make sure you passed an AsyncOpenAI client."
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)
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return self.alist_batches( # type: ignore
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openai_client=openai_client, after=after, limit=limit
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)
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response = openai_client.batches.list(after=after, limit=limit)
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return response
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class OpenAIAssistantsAPI(BaseLLM):
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